realp

Real tunable parameter

Syntax

p = realp(paramname,initvalue)

Description

p = realp(paramname,initvalue) creates a tunable real-valued parameter with name specified by paramname and initial value initvalue. Tunable real parameters can be scalar- or matrix- valued.

Input Arguments

`paramname`	Name of the `realp` parameter `p`, specified as a character vector such as `'a'` or `'zeta'`. This input argument sets the value of the `Name` property of `p`.
`initvalue`	Initial numeric value of the parameter `p`. `initvalue` can be a real scalar value or a 2-dimensional matrix.

Output Arguments

`p`	`realp` parameter object.

Properties

`Name`	Name of the `realp` parameter object, stored as a character vector. The value of `Name` is set by the `paramname` input argument to `realp` and cannot be changed.
`Value`	Value of the tunable parameter. `Value` can be a real scalar value or a 2-dimensional matrix. The initial value is set by the `initvalue` input argument. The dimensions of `Value` are fixed on creation of the `realp` object.
`Minimum`	Lower bound for the parameter value. The dimension of the `Minimum` property matches the dimension of the `Value` property. For matrix-valued parameters, use indexing to specify lower bounds on individual elements: p = realp('K',eye(2)); p.Minimum([1 4]) = -5; Use scalar expansion to set the same lower bound for all matrix elements: p.Minimum = -5; Default: `-Inf` for all entries
`Maximum`	Upper bound for the parameter value. The dimension of the `Maximum` property matches the dimension of the `Value` property. For matrix-valued parameters, use indexing to specify upper bounds on individual elements: p = realp('K',eye(2)); p.Maximum([1 4]) = 5; Use scalar expansion to set the same upper bound for all matrix elements: p.Maximum = 5; Default: `Inf` for all entries
`Free`	Boolean value specifying whether the parameter is free to be tuned. Set the `Free` property to 1 (`true`) for tunable parameters, and 0 (`false`) for fixed parameters. The dimension of the `Free` property matches the dimension of the `Value` property. Default: 1 (`true`) for all entries

Examples

collapse all

Tunable Low-Pass Filter

Open Live Script

In this example, you will create a low-pass filter with one tunable parameter a:

$F = \frac{a}{s + a}$

Since the numerator and denominator coefficients of a tunableTF block are independent, you cannot use tunableTF to represent F. Instead, construct F using the tunable real parameter object realp.

Create a real tunable parameter with an initial value of 10.

a = realp('a',10)

a = 
       Name: 'a'
      Value: 10
    Minimum: -Inf
    Maximum: Inf
       Free: 1

Real scalar parameter.

Use tf to create the tunable low-pass filter F.

numerator = a;
denominator = [1,a];
F = tf(numerator,denominator)

F =

  Generalized continuous-time state-space model with 1 outputs, 1 inputs, 1 states, and the following blocks:
    a: Scalar parameter, 2 occurrences.

Type "ss(F)" to see the current value, "get(F)" to see all properties, and "F.Blocks" to interact with the blocks.

F is a genss object which has the tunable parameter a in its Blocks property. You can connect F with other tunable or numeric models to create more complex control system models. For an example, see Control System with Tunable Components.

Create Parametric Diagonal Matrix

Open Live Script

Create a matrix with tunable diagonal elements and with off-diagonal elements fixed to zero.

Create a parametric matrix whose initial value is the identity matrix.

p = realp('P',eye(2));

p is a 2-by-2 parametric matrix. Since the initial value is the identity matrix, the off-diagonal initial values are zero.

Fix the values of the off-diagonal elements by setting the Free property to false.

p.Free(1,2) = false;
p.Free(2,1) = false;

Tips

Use arithmetic operators (+, -, *, /, \, and ^) to combine realp objects into rational expressions or matrix expressions. You can use the resulting expressions in model-creation functions such as tf, zpk, and ss to create tunable models. For more information about tunable models, see Models with Tunable Coefficients in the Control System Toolbox™ User's Guide.

Documentation